Support Vector Regression for Data-Driven Decision Making
Abstract
Support Vector Regression (SVR) is a robust machine learning technique adapted from the Support Vector Machine (SVM) algorithm, specifically designed for predicting continuous outcomes. Unlike traditional regression models that aim to minimize error directly, SVR introduces the concept of an ?-insensitive margin, allowing flexibility in prediction within a controlled tolerance. This mathematical innovation makes SVR particularly suitable for business environments where data is often noisy and complex. In this paper, we provide a brief but comprehensive overview of SVR, outlining its theoretical foundation, optimization conditions, and core mathematical formulations. The paper also highlights SVR’s capacity to model nonlinear relationships through kernel functions, offering an advanced solution for data-driven decision-making. The effectiveness of SVR is demonstrated through its application in future business data analysis.
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